def _create_counts_uncertainty_vector_uint32(height, standard_name): default_array = DefaultData.create_default_vector(height, np.float32) variable = Variable(["y"], default_array) tu.add_encoding(variable, np.uint32, DefaultData.get_default_fill_value(np.uint32), 0.01) variable.attrs["standard_name"] = standard_name tu.add_units(variable, "count") return variable
def _create_int32_vector(height, standard_name=None, long_name=None, orig_name=None): default_array = DefaultData.create_default_vector(height, np.int32) variable = Variable(["y"], default_array) tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.int32)) HIRS._set_name_attributes(long_name, orig_name, standard_name, variable) return variable
def _create_angle_variable_int(scale_factor, standard_name=None, long_name=None, unsigned=False, fill_value=None): default_array = DefaultData.create_default_array(TIE_SIZE, TIE_SIZE, np.float32, fill_value=np.NaN) variable = Variable(["y_tie", "x_tie"], default_array) if unsigned is True: data_type = np.uint16 else: data_type = np.int16 if fill_value is None: fill_value = DefaultData.get_default_fill_value(data_type) if standard_name is not None: variable.attrs["standard_name"] = standard_name if long_name is not None: variable.attrs["long_name"] = long_name tu.add_units(variable, "degree") variable.attrs["tie_points"] = "true" tu.add_encoding(variable, data_type, fill_value, scale_factor, chunksizes=CHUNKSIZES) return variable
def create_float_variable(width, height, standard_name=None, long_name=None, dim_names=None, fill_value=None): if fill_value is None: default_array = DefaultData.create_default_array( width, height, np.float32) else: default_array = DefaultData.create_default_array( width, height, np.float32, fill_value=fill_value) if dim_names is None: variable = Variable(["y", "x"], default_array) else: variable = Variable(dim_names, default_array) if fill_value is None: variable.attrs["_FillValue"] = DefaultData.get_default_fill_value( np.float32) else: variable.attrs["_FillValue"] = fill_value if standard_name is not None: variable.attrs["standard_name"] = standard_name if long_name is not None: variable.attrs["long_name"] = long_name return variable
def _create_scaled_int16_vector(height, standard_name=None, original_name=None, long_name=None, scale_factor=0.01): default_array = DefaultData.create_default_vector(height, np.float32) variable = Variable(["y"], default_array) tu.add_encoding(variable, np.int16, DefaultData.get_default_fill_value(np.int16), scale_factor) HIRS._set_name_attributes(long_name, original_name, standard_name, variable) return variable
def _assert_angle_variables(self, ds): satellite_zenith_angle = ds.variables["satellite_zenith_angle"] self.assertEqual((6, ), satellite_zenith_angle.shape) self.assertTrue(np.isnan(satellite_zenith_angle.data[3])) self.assertEqual(np.uint16, satellite_zenith_angle.encoding['dtype']) self.assertEqual(DefaultData.get_default_fill_value(np.uint16), satellite_zenith_angle.encoding['_FillValue']) self.assertEqual(0.01, satellite_zenith_angle.encoding['scale_factor']) self.assertEqual(-180.0, satellite_zenith_angle.encoding['add_offset']) self.assertEqual("platform_zenith_angle", satellite_zenith_angle.attrs["standard_name"]) self.assertEqual("degree", satellite_zenith_angle.attrs["units"]) self.assertEqual("longitude latitude", satellite_zenith_angle.attrs["coordinates"]) solar_azimuth_angle = ds.variables["solar_azimuth_angle"] self.assertEqual((6, 56), solar_azimuth_angle.shape) self.assertTrue(np.isnan(solar_azimuth_angle.data[4, 4])) self.assertEqual(np.uint16, solar_azimuth_angle.encoding['dtype']) self.assertEqual(DefaultData.get_default_fill_value(np.uint16), solar_azimuth_angle.encoding['_FillValue']) self.assertEqual(0.01, solar_azimuth_angle.encoding['scale_factor']) self.assertEqual(-180.0, solar_azimuth_angle.encoding['add_offset']) self.assertEqual(CHUNKING_2D, solar_azimuth_angle.encoding['chunksizes']) self.assertEqual("solar_azimuth_angle", solar_azimuth_angle.attrs["standard_name"]) self.assertEqual("degree", solar_azimuth_angle.attrs["units"]) self.assertEqual("longitude latitude", solar_azimuth_angle.attrs["coordinates"])
def add_easy_fcdr_variables(dataset, height, corr_dx=None, corr_dy=None, lut_size=None): default_array = DefaultData.create_default_array_3d(SWATH_WIDTH, height, NUM_CHANNELS, np.float32, fill_value=np.NaN) variable = Variable(["channel", "y", "x"], default_array) tu.add_fill_value(variable, np.NaN) tu.add_units(variable, "K") variable.attrs["long_name"] = "independent uncertainty per pixel" dataset["u_independent_tb"] = variable default_array = DefaultData.create_default_array_3d(SWATH_WIDTH, height, NUM_CHANNELS, np.float32, fill_value=np.NaN) variable = Variable(["channel", "y", "x"], default_array) tu.add_fill_value(variable, np.NaN) tu.add_units(variable, "K") variable.attrs["long_name"] = "structured uncertainty per pixel" dataset["u_structured_tb"] = variable
def add_gridded_geolocation_variables(dataset, width, height): default_array = DefaultData.create_default_vector(height, np.float32, fill_value=np.NaN) variable = Variable(["y"], default_array) TemplateUtil.add_fill_value(variable, np.NaN) variable.attrs["standard_name"] = LAT_NAME variable.attrs["long_name"] = LAT_NAME variable.attrs["bounds"] = "lat_bnds" TemplateUtil.add_units(variable, LATITUDE_UNIT) dataset["lat"] = variable default_array = DefaultData.create_default_array(2, height, np.float32, fill_value=np.NaN) variable = Variable(["y", "bounds"], default_array) TemplateUtil.add_fill_value(variable, np.NaN) variable.attrs["long_name"] = "latitude cell boundaries" TemplateUtil.add_units(variable, LATITUDE_UNIT) dataset["lat_bnds"] = variable default_array = DefaultData.create_default_vector(width, np.float32, fill_value=np.NaN) variable = Variable(["x"], default_array) TemplateUtil.add_fill_value(variable, np.NaN) variable.attrs["standard_name"] = LON_NAME variable.attrs["long_name"] = LON_NAME TemplateUtil.add_units(variable, LONGITUDE_UNIT) variable.attrs["bounds"] = "lon_bnds" dataset["lon"] = variable default_array = DefaultData.create_default_array(2, width, np.float32, fill_value=np.NaN) variable = Variable(["x", "bounds"], default_array) TemplateUtil.add_fill_value(variable, np.NaN) TemplateUtil.add_units(variable, LONGITUDE_UNIT) variable.attrs["long_name"] = "longitude cell boundaries" dataset["lon_bnds"] = variable
def _create_refl_uncertainty_variable(height, long_name=None, structured=False): default_array = DefaultData.create_default_array(SWATH_WIDTH, height, np.float32, fill_value=np.NaN) variable = Variable(["y", "x"], default_array) tu.add_units(variable, "percent") tu.add_geolocation_attribute(variable) variable.attrs["long_name"] = long_name if structured: tu.add_encoding(variable, np.int16, DefaultData.get_default_fill_value(np.int16), 0.01, chunksizes=CHUNKS_2D) variable.attrs["valid_min"] = 3 variable.attrs["valid_max"] = 5 else: tu.add_encoding(variable, np.int16, DefaultData.get_default_fill_value(np.int16), 0.00001, chunksizes=CHUNKS_2D) variable.attrs["valid_max"] = 1000 variable.attrs["valid_min"] = 10 return variable
def add_original_variables(dataset, height, srf_size=None): # height is ignored - supplied just for interface compatibility tb 2017-07-19 # latitude_vis default_array = DefaultData.create_default_array(FULL_SIZE, FULL_SIZE, np.float32, fill_value=np.NaN) variable = Variable(["y", "x"], default_array) variable.attrs["standard_name"] = "latitude" tu.add_units(variable, "degrees_north") tu.add_encoding(variable, np.int16, -32768, scale_factor=0.0027466658) dataset["latitude_vis"] = variable # longitude_vis default_array = DefaultData.create_default_array(FULL_SIZE, FULL_SIZE, np.float32, fill_value=np.NaN) variable = Variable(["y", "x"], default_array) variable.attrs["standard_name"] = "longitude" tu.add_units(variable, "degrees_east") tu.add_encoding(variable, np.int16, -32768, scale_factor=0.0054933317) dataset["longitude_vis"] = variable # latitude_ir_wv default_array = DefaultData.create_default_array(IR_SIZE, IR_SIZE, np.float32, fill_value=np.NaN) variable = Variable([IR_X_DIMENSION, IR_X_DIMENSION], default_array) variable.attrs["standard_name"] = "latitude" tu.add_units(variable, "degrees_north") tu.add_encoding(variable, np.int16, -32768, scale_factor=0.0027466658) dataset["latitude_ir_wv"] = variable # longitude_ir_wv default_array = DefaultData.create_default_array(IR_SIZE, IR_SIZE, np.float32, fill_value=np.NaN) variable = Variable([IR_X_DIMENSION, IR_X_DIMENSION], default_array) variable.attrs["standard_name"] = "longitude" tu.add_units(variable, "degrees_east") tu.add_encoding(variable, np.int16, -32768, scale_factor=0.0054933317) dataset["longitude_ir_wv"] = variable
def add_variables(dataset, width, height): tu.add_geolocation_variables(dataset, width, height, chunksizes=CHUNKING) tu.add_quality_flags(dataset, width, height, chunksizes=CHUNKING) default_array = DefaultData.create_default_vector( height, np.int32, fill_value=4294967295) variable = Variable(["y"], default_array) tu.add_fill_value(variable, 4294967295) variable.attrs["standard_name"] = "time" variable.attrs[ "long_name"] = "Acquisition time in seconds since 1970-01-01 00:00:00" tu.add_units(variable, "s") dataset["time"] = variable default_array = DefaultData.create_default_array(width, height, np.float32, fill_value=np.NaN) variable = Variable(["y", "x"], default_array) tu.add_fill_value(variable, np.NaN) variable.attrs["coordinates"] = "longitude latitude" dataset["aot"] = variable dataset["u_independent_aot"] = tu.create_CDR_uncertainty( width, height, "Uncertainty of aot due to independent effects") dataset["u_structured_aot"] = tu.create_CDR_uncertainty( width, height, "Uncertainty of aot due to structured effects") dataset["u_common_aot"] = tu.create_CDR_uncertainty( width, height, "Uncertainty of aot due to common effects")
def add_geolocation_variables(dataset, width, height, chunksizes=None): default_array = DefaultData.create_default_array(width, height, np.float32, fill_value=np.NaN) variable = Variable(["y", "x"], default_array) variable.attrs["standard_name"] = LAT_NAME TemplateUtil.add_units(variable, LATITUDE_UNIT) TemplateUtil.add_encoding(variable, np.int16, -32768, scale_factor=0.0027466658, chunksizes=chunksizes) dataset[LAT_NAME] = variable default_array = DefaultData.create_default_array(width, height, np.float32, fill_value=np.NaN) variable = Variable(["y", "x"], default_array) variable.attrs["standard_name"] = LON_NAME TemplateUtil.add_units(variable, LONGITUDE_UNIT) TemplateUtil.add_encoding(variable, np.int16, -32768, scale_factor=0.0054933317, chunksizes=chunksizes) dataset[LON_NAME] = variable
def assert_common_angles(self, ds, chunking=None): satellite_zenith_angle = ds.variables["satellite_zenith_angle"] self.assertEqual((6, 56), satellite_zenith_angle.shape) self.assertTrue(np.isnan(satellite_zenith_angle.data[2, 2])) self.assertEqual(np.uint16, satellite_zenith_angle.encoding['dtype']) self.assertEqual(DefaultData.get_default_fill_value(np.uint16), satellite_zenith_angle.encoding['_FillValue']) self.assertEqual(0.01, satellite_zenith_angle.encoding['scale_factor']) self.assertEqual(0, satellite_zenith_angle.encoding['add_offset']) if chunking is not None: self.assertEqual(chunking, satellite_zenith_angle.encoding['chunksizes']) self.assertEqual("platform_zenith_angle", satellite_zenith_angle.attrs["standard_name"]) self.assertEqual("degree", satellite_zenith_angle.attrs["units"]) self.assertEqual("longitude latitude", satellite_zenith_angle.attrs["coordinates"]) self.assertEqual([0, 180], satellite_zenith_angle.attrs["valid_range"]) solar_zenith_angle = ds.variables["solar_zenith_angle"] self.assertEqual((6, 56), solar_zenith_angle.shape) self.assertTrue(np.isnan(solar_zenith_angle.data[3, 3])) self.assertEqual(np.uint16, solar_zenith_angle.encoding['dtype']) self.assertEqual(DefaultData.get_default_fill_value(np.uint16), solar_zenith_angle.encoding['_FillValue']) self.assertEqual(0.01, solar_zenith_angle.encoding['scale_factor']) self.assertEqual(0, solar_zenith_angle.encoding['add_offset']) if chunking is not None: self.assertEqual(chunking, solar_zenith_angle.encoding['chunksizes']) self.assertEqual("solar_zenith_angle", solar_zenith_angle.attrs["standard_name"]) self.assertEqual("solar_zenith_angle", solar_zenith_angle.attrs["orig_name"]) self.assertEqual("degree", solar_zenith_angle.attrs["units"]) self.assertEqual("longitude latitude", solar_zenith_angle.attrs["coordinates"]) self.assertEqual([0, 180], solar_zenith_angle.attrs["valid_range"]) satellite_azimuth_angle = ds.variables["satellite_azimuth_angle"] self.assertEqual((6, 56), satellite_azimuth_angle.shape) self.assertTrue(np.isnan(satellite_azimuth_angle.data[5, 5])) self.assertEqual(np.uint16, satellite_azimuth_angle.encoding['dtype']) self.assertEqual(DefaultData.get_default_fill_value(np.uint16), satellite_azimuth_angle.encoding['_FillValue']) self.assertEqual(0.01, satellite_azimuth_angle.encoding['scale_factor']) self.assertEqual(0, satellite_azimuth_angle.encoding['add_offset']) if chunking is not None: self.assertEqual(chunking, satellite_azimuth_angle.encoding['chunksizes']) self.assertEqual("sensor_azimuth_angle", satellite_azimuth_angle.attrs["standard_name"]) self.assertEqual([0, 360], satellite_azimuth_angle.attrs["valid_range"]) self.assertEqual("clockwise from north", satellite_azimuth_angle.attrs["comment"]) self.assertEqual("degree", satellite_azimuth_angle.attrs["units"]) self.assertEqual("longitude latitude", satellite_azimuth_angle.attrs["coordinates"]) solar_azimuth_angle = ds.variables["solar_azimuth_angle"] self.assertEqual((6, 56), solar_azimuth_angle.shape) self.assertTrue(np.isnan(solar_azimuth_angle.data[4, 4])) self.assertEqual(np.uint16, solar_azimuth_angle.encoding['dtype']) self.assertEqual(DefaultData.get_default_fill_value(np.uint16), solar_azimuth_angle.encoding['_FillValue']) self.assertEqual(0.01, solar_azimuth_angle.encoding['scale_factor']) self.assertEqual(0, solar_azimuth_angle.encoding['add_offset']) if chunking is not None: self.assertEqual(chunking, solar_azimuth_angle.encoding['chunksizes']) self.assertEqual("solar_azimuth_angle", solar_azimuth_angle.attrs["standard_name"]) self.assertEqual([0, 360], solar_azimuth_angle.attrs["valid_range"]) self.assertEqual("clockwise from north", solar_azimuth_angle.attrs["comment"]) self.assertEqual("degree", solar_azimuth_angle.attrs["units"]) self.assertEqual("longitude latitude", solar_azimuth_angle.attrs["coordinates"])
def add_common_sensor_variables(dataset, height, srf_size): # scanline default_array = DefaultData.create_default_vector(height, np.int16) variable = Variable(["y"], default_array) tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.int16)) variable.attrs["long_name"] = "scanline_number" tu.add_units(variable, "count") dataset["scanline"] = variable # time default_array = DefaultData.create_default_vector(height, np.uint32) variable = Variable(["y"], default_array) tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.uint32)) variable.attrs["standard_name"] = "time" variable.attrs["long_name"] = "Acquisition time in seconds since 1970-01-01 00:00:00" tu.add_units(variable, "s") dataset["time"] = variable # quality_scanline_bitmask default_array = DefaultData.create_default_vector(height, np.int32, fill_value=0) variable = Variable(["y"], default_array) variable.attrs["standard_name"] = "status_flag" variable.attrs["long_name"] = "quality_indicator_bitfield" variable.attrs[ "flag_masks"] = "1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 65536, 131072, 262144, 524288, 1048576, 2097152, 4194304, 8388608, 16777216, 33554432, 67108864, 134217728, 268435456, 536870912 1073741824" variable.attrs[ "flag_meanings"] = "do_not_use_scan time_sequence_error data_gap_preceding_scan no_calibration no_earth_location clock_update status_changed line_incomplete, time_field_bad time_field_bad_not_inf inconsistent_sequence scan_time_repeat uncalib_bad_time calib_few_scans uncalib_bad_prt calib_marginal_prt uncalib_channels uncalib_inst_mode quest_ant_black_body zero_loc bad_loc_time bad_loc_marginal bad_loc_reason bad_loc_ant reduced_context bad_temp_no_rself" dataset["quality_scanline_bitmask"] = variable default_array = DefaultData.create_default_array(srf_size, NUM_CHANNELS, np.float32, fill_value=np.NaN) variable = Variable(["channel", "n_frequencies"], default_array) variable.attrs["long_name"] = 'Spectral Response Function weights' variable.attrs["description"] = 'Per channel: weights for the relative spectral response function' tu.add_encoding(variable, np.int16, -32768, 0.000033) dataset['SRF_weights'] = variable default_array = DefaultData.create_default_array(srf_size, NUM_CHANNELS, np.float32, fill_value=np.NaN) variable = Variable(["channel", "n_frequencies"], default_array) variable.attrs["long_name"] = 'Spectral Response Function wavelengths' variable.attrs["description"] = 'Per channel: wavelengths for the relative spectral response function' tu.add_encoding(variable, np.int32, -2147483648, 0.0001) tu.add_units(variable, "um") dataset['SRF_wavelengths'] = variable default_vector = DefaultData.create_default_vector(height, np.uint8, fill_value=255) variable = Variable(["y"], default_vector) tu.add_fill_value(variable, 255) variable.attrs["long_name"] = 'Indicator of original file' variable.attrs[ "description"] = "Indicator for mapping each line to its corresponding original level 1b file. See global attribute 'source' for the filenames. 0 corresponds to 1st listed file, 1 to 2nd file." dataset["scanline_map_to_origl1bfile"] = variable default_vector = DefaultData.create_default_vector(height, np.int16, fill_value=DefaultData.get_default_fill_value(np.int16)) variable = Variable(["y"], default_vector) tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.int16)) variable.attrs["long_name"] = 'Original_Scan_line_number' variable.attrs["description"] = 'Original scan line numbers from corresponding l1b records' dataset["scanline_origl1b"] = variable
def _create_easy_fcdr_variable(height, long_name): default_array = DefaultData.create_default_array_3d(SWATH_WIDTH, height, NUM_CHANNELS, np.float32, np.NaN) variable = Variable(["channel", "y", "x"], default_array) tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.001, chunksizes=CHUNKING_BT) variable.attrs["long_name"] = long_name tu.add_units(variable, "K") tu.add_geolocation_attribute(variable) variable.attrs["valid_min"] = 1 variable.attrs["valid_max"] = 65534 return variable
def _create_float32_vector(fill_value, height, long_name, orig_name): default_array = DefaultData.create_default_vector(height, np.float32, fill_value=fill_value) variable = Variable(["y"], default_array) if fill_value is None: tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.float32)) else: tu.add_fill_value(variable, fill_value) variable.attrs["long_name"] = long_name if orig_name is not None: variable.attrs["orig_name"] = orig_name return variable
def _assert_correct_counts_variable(self, ds, name, long_name): variable = ds.variables[name] self.assertEqual((5, 409), variable.shape) self.assertEqual(DefaultData.get_default_fill_value(np.int32), variable.data[3, 306]) self.assertEqual(DefaultData.get_default_fill_value(np.int32), variable.attrs["_FillValue"]) self.assertEqual(long_name, variable.attrs["long_name"]) self.assertEqual("count", variable.attrs["units"]) self.assertEqual("longitude latitude", variable.attrs["coordinates"]) self.assertEqual(CHUNKING, variable.encoding["chunksizes"])
def add_common_sensor_variables(dataset, height, srf_size): # scanline default_array = DefaultData.create_default_vector(height, np.int16) variable = Variable(["y"], default_array) tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.int16)) variable.attrs["long_name"] = "scanline_number" tu.add_units(variable, "count") dataset["scanline"] = variable # time default_array = DefaultData.create_default_vector(height, np.datetime64) variable = Variable(["y"], default_array) tu.add_fill_value(variable, 4294967295) variable.attrs["standard_name"] = "time" variable.attrs["long_name"] = "Acquisition time in seconds since 1970-01-01 00:00:00" # do not set 'units' of "_FillValue" here, xarray sets this from encoding upon storing the file tu.add_encoding(variable, np.uint32, None, scale_factor=0.1) variable.encoding["units"] = "seconds since 1970-01-01 00:00:00" # encoding 'add_offset' varies per file and either needs to be set # by the user or intelligently in fiduceo.fcdr.writer.fcdr_writer.FCDRWriter.write dataset["time"] = variable # quality_scanline_bitmask default_array = DefaultData.create_default_vector(height, np.int32, fill_value=0) variable = Variable(["y"], default_array) variable.attrs["standard_name"] = "status_flag" variable.attrs["long_name"] = "quality_indicator_bitfield" variable.attrs[ "flag_masks"] = "1, 2, 4, 8, 16" variable.attrs["flag_meanings"] = "do_not_use_scan reduced_context bad_temp_no_rself suspect_geo suspect_time" dataset["quality_scanline_bitmask"] = variable default_array = DefaultData.create_default_array(srf_size, NUM_CHANNELS, np.float32, fill_value=np.NaN) variable = Variable(["channel", "n_wavelengths"], default_array) variable.attrs["long_name"] = 'Spectral Response Function weights' variable.attrs["description"] = 'Per channel: weights for the relative spectral response function' tu.add_encoding(variable, np.int16, -32768, 0.000033) dataset['SRF_weights'] = variable default_array = DefaultData.create_default_array(srf_size, NUM_CHANNELS, np.float32, fill_value=np.NaN) variable = Variable(["channel", "n_wavelengths"], default_array) variable.attrs["long_name"] = 'Spectral Response Function wavelengths' variable.attrs["description"] = 'Per channel: wavelengths for the relative spectral response function' tu.add_encoding(variable, np.int32, -2147483648, 0.0001) tu.add_units(variable, "um") dataset['SRF_wavelengths'] = variable default_vector = DefaultData.create_default_vector(height, np.uint8, fill_value=255) variable = Variable(["y"], default_vector) tu.add_fill_value(variable, 255) variable.attrs["long_name"] = 'Indicator of original file' variable.attrs[ "description"] = "Indicator for mapping each line to its corresponding original level 1b file. See global attribute 'source' for the filenames. 0 corresponds to 1st listed file, 1 to 2nd file." dataset["scanline_map_to_origl1bfile"] = variable default_vector = DefaultData.create_default_vector(height, np.int16, fill_value=DefaultData.get_default_fill_value(np.int16)) variable = Variable(["y"], default_vector) tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.int16)) variable.attrs["long_name"] = 'Original_Scan_line_number' variable.attrs["description"] = 'Original scan line numbers from corresponding l1b records' dataset["scanline_origl1b"] = variable
def _create_overpass_counts_variable(height, width, description): fill_value = DefaultData.get_default_fill_value(np.uint8) default_array = DefaultData.create_default_array(width, height, np.uint8, fill_value=fill_value) variable = Variable(["y", "x"], default_array) tu.add_fill_value(variable, fill_value) variable.attrs["description"] = description variable.attrs["coordinates"] = "lon lat" return variable
def _create_counts_variable(height, long_name): default_array = DefaultData.create_default_array( SWATH_WIDTH, height, np.int32) variable = Variable(["y", "x"], default_array) tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.int32)) variable.attrs["long_name"] = long_name tu.add_units(variable, "count") tu.add_geolocation_attribute(variable) tu.add_chunking(variable, CHUNKS_2D) return variable
def _create_geo_angle_variable(standard_name, height, orig_name=None, chunking=None): default_array = DefaultData.create_default_array(SWATH_WIDTH, height, np.float32, fill_value=np.NaN) variable = Variable(["y", "x"], default_array) variable.attrs["standard_name"] = standard_name if orig_name is not None: variable.attrs["orig_name"] = orig_name tu.add_units(variable, "degree") tu.add_geolocation_attribute(variable) tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01, -180.0, chunking) return variable
def add_lookup_tables(dataset, num_channels, lut_size): default_array = DefaultData.create_default_array(num_channels, lut_size, np.float32, fill_value=np.NaN) variable = Variable(["lut_size", "channel"], default_array) TemplateUtil.add_fill_value(variable, np.NaN) variable.attrs["description"] = "Lookup table to convert radiance to brightness temperatures" dataset['lookup_table_BT'] = variable default_array = DefaultData.create_default_array(num_channels, lut_size, np.float32, fill_value=np.NaN) variable = Variable(["lut_size", "channel"], default_array) TemplateUtil.add_fill_value(variable, np.NaN) variable.attrs["description"] = "Lookup table to convert brightness temperatures to radiance" dataset['lookup_table_radiance'] = variable
def add_correlation_coefficients(dataset, num_channels, delta_x, delta_y): default_array = DefaultData.create_default_array(num_channels, delta_x, np.float32, fill_value=np.NaN) variable = Variable(["delta_x", "channel"], default_array) TemplateUtil.add_fill_value(variable, np.NaN) variable.attrs["long_name"] = "cross_element_correlation_coefficients" variable.attrs["description"] = "Correlation coefficients per channel for scanline correlation" dataset['cross_element_correlation_coefficients'] = variable default_array = DefaultData.create_default_array(num_channels, delta_y, np.float32, fill_value=np.NaN) variable = Variable(["delta_y", "channel"], default_array) TemplateUtil.add_fill_value(variable, np.NaN) variable.attrs["long_name"] = "cross_line_correlation_coefficients" variable.attrs["description"] = "Correlation coefficients per channel for inter scanline correlation" dataset['cross_line_correlation_coefficients'] = variable
def _add_angle_variables(dataset, height): default_array = DefaultData.create_default_vector(height, np.float32, fill_value=np.NaN) variable = Variable(["y"], default_array) variable.attrs["standard_name"] = "platform_zenith_angle" tu.add_units(variable, "degree") tu.add_geolocation_attribute(variable) tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01, -180.0) dataset["satellite_zenith_angle"] = variable dataset["solar_azimuth_angle"] = HIRS._create_geo_angle_variable( "solar_azimuth_angle", height, chunking=CHUNKING_2D)
def add_full_fcdr_variables(dataset, height): # u_btemps variable = AMSUB_MHS._create_3d_float_variable(height) variable.attrs[ "long_name"] = "total uncertainty of brightness temperature" tu.add_units(variable, "K") dataset["u_btemps"] = variable # u_syst_btemps variable = AMSUB_MHS._create_3d_float_variable(height) variable.attrs[ "long_name"] = "systematic uncertainty of brightness temperature" tu.add_units(variable, "K") dataset["u_syst_btemps"] = variable # u_random_btemps variable = AMSUB_MHS._create_3d_float_variable(height) variable.attrs["long_name"] = "noise on brightness temperature" tu.add_units(variable, "K") dataset["u_random_btemps"] = variable # u_instrtemp default_array = DefaultData.create_default_vector(height, np.float32, fill_value=np.NaN) variable = Variable(["y"], default_array) tu.add_fill_value(variable, np.NaN) variable.attrs["long_name"] = "uncertainty of instrument temperature" tu.add_units(variable, "K") dataset["u_instrtemp"] = variable # u_latitude variable = AMSUB_MHS.create_angle_uncertainty_variable( "latitude", height) dataset["u_latitude"] = variable # u_longitude variable = AMSUB_MHS.create_angle_uncertainty_variable( "longitude", height) dataset["u_longitude"] = variable # u_satellite_azimuth_angle variable = AMSUB_MHS.create_angle_uncertainty_variable( "satellite azimuth angle", height) dataset["u_satellite_azimuth_angle"] = variable # u_satellite_zenith_angle variable = AMSUB_MHS.create_angle_uncertainty_variable( "satellite zenith angle", height) dataset["u_satellite_zenith_angle"] = variable # u_solar_azimuth_angle variable = AMSUB_MHS.create_angle_uncertainty_variable( "solar azimuth angle", height) dataset["u_solar_azimuth_angle"] = variable # u_solar_zenith_angle variable = AMSUB_MHS.create_angle_uncertainty_variable( "solar zenith angle", height) dataset["u_solar_zenith_angle"] = variable
def _assert_line_int32_variable(self, ds, name, standard_name=None, long_name=None, orig_name=None): variable = ds.variables[name] self.assertEqual((7, ), variable.shape) self.assertEqual(DefaultData.get_default_fill_value(np.int32), variable.data[4]) self.assertEqual(DefaultData.get_default_fill_value(np.int32), variable.attrs["_FillValue"]) self._assert_name_attributes(variable, standard_name, long_name, orig_name) return variable
def add_quality_flags(dataset, width, height, chunksizes=None, masks_append=None, meanings_append=None): default_array = DefaultData.create_default_array(width, height, np.uint8, fill_value=0) variable = Variable(["y", "x"], default_array) variable.attrs["standard_name"] = "status_flag" TemplateUtil.add_geolocation_attribute(variable) masks = "1, 2, 4, 8, 16, 32, 64, 128" if masks_append is not None: masks = masks + masks_append variable.attrs["flag_masks"] = masks meanings = "invalid use_with_caution invalid_input invalid_geoloc invalid_time sensor_error padded_data incomplete_channel_data" if meanings_append is not None: meanings = meanings + meanings_append variable.attrs["flag_meanings"] = meanings if chunksizes is not None: TemplateUtil.add_chunking(variable, chunksizes) dataset["quality_pixel_bitmask"] = variable
def test_add_geolocation_attribute(self): default_array = DefaultData.create_default_array_3d( 8, 6, 4, np.float32, np.NaN) variable = Variable(["channel", "y", "x"], default_array) TemplateUtil.add_geolocation_attribute(variable) self.assertEqual("longitude latitude", variable.attrs["coordinates"])
def _create_bt_uncertainty_variable(height, long_name): default_array = DefaultData.create_default_array(SWATH_WIDTH, height, np.float32, fill_value=np.NaN) variable = Variable(["y", "x"], default_array) tu.add_units(variable, "K") tu.add_geolocation_attribute(variable) tu.add_encoding(variable, np.int16, DefaultData.get_default_fill_value(np.int16), 0.001, chunksizes=CHUNKS_2D) variable.attrs["valid_max"] = 15000 variable.attrs["valid_min"] = 1 variable.attrs["long_name"] = long_name return variable
def test__get_add_offset_missing(self): default_array = DefaultData.create_default_vector(2, np.float32) variable = Variable(["y"], default_array) variable.encoding = dict([('dtype', np.int8), ('_FillValue', -127), ('scale_factor', 0.023)]) add_offset = DataUtility._get_add_offset(variable) self.assertEqual(0.0, add_offset)